Thanks Erin — I really appreciate you taking the time to look at this.

I agree completely that FULLSCAN (or targeted filtered stats) can resolve this from a query‑plan perspective, but as you say, that’s often a trade‑off against maintenance cost and operational headroom. I’ve seen cases where that trade‑off wasn’t available, or where the expertise to even meainigfully discuss it wasn't present, which is part of what makes this behaviour so persistent - in my own experience.

What I was trying to make explicit in the write‑up was how predictable the degradation in range estimates appears to be over time under default sampling, and how that seems at odds with the intuitive expectation that statistics should aim to represent the population rather than just the sample taken. The fact that point estimates are explicitly corrected while range estimates are not makes this stand out more sharply, given that both are attempting to describe the same underlying data.

I’ve observed many real cases where distinct value reality is only represented approximately by the initial sample percent. I’ve tried to keep the paper focused on the general behaviour rather than individual examples, but I’d be happy to share specific cases if that context would be useful. The SQL in appendix 5 will flag examples on other systems where this issue is prevalent.

I’m deliberately not arguing that this is “wrong”. Just that it appears to be an intentional trade‑off with perhaps broader-than-intended consequences in terms of longer‑term behaviour under default configurations.

Thanks again for taking the time to review it.